Brain small vessel blood flow monitoring method and system based on ultrasonic imaging

Abstract

The invention relates to a brain small vessel blood flow monitoring method and system based on ultrasonic imaging, and the method comprises the steps: setting pulse Doppler ultrasonic imaging devices at different positions, and carrying out the color pulse Doppler imaging of the artery and vein in a brain small vessel through the pulse Doppler ultrasonic imaging devices, arranging according to spatial positions corresponding to different positions to obtain three-dimensional color pulse Doppler imaging; in the process of color pulse Doppler imaging, comparing and analyzing a current frame of color pulse Doppler image and a previous frame of color pulse Doppler image to obtain an analysis result for optimizing a subsequent color pulse Doppler image; blood flow power information is extracted from the three-dimensional color pulse Doppler imaging, and the blood flow change condition of the small cerebral vessels is obtained based on the blood flow power information. The brain small vessel blood flow monitoring system can realize real-time and dynamic monitoring of brain small vessel blood flow, has high clinical application value, and provides a new technical means for prevention and treatment work of brain small vessel diseases.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ultrasonic imaging, in particular to a cerebral small vessel blood flow monitoring method and system based on ultrasonic imaging. BACKGROUND

[0002] Cerebral small vessel disease is one of the important factors leading to cognitive decline, stroke and other serious brain diseases. Early and accurate monitoring of cerebral small vessel blood flow changes is of great significance for disease prevention, diagnosis and treatment. Cerebral small vessels mainly include arterioles and venules, and their structural and functional abnormalities are closely related to various brain diseases. These diseases not only seriously affect the quality of life of patients, but also bring a heavy burden to society and families.

[0003] Traditional cerebral blood flow monitoring methods, such as magnetic resonance imaging (MRI) and computed tomography (CT), can provide relatively accurate cerebral blood vessel images, but have some limitations. MRI equipment is expensive, the examination time is long, the patient's cooperation degree is high, and it is contraindicated for patients with metal implants in the body; CT has fast examination speed, but it has radiation exposure and limited resolution for small vessels, making it difficult to clearly show the details of cerebral small vessels. These factors limit their widespread application in cerebral small vessel blood flow monitoring, making it difficult to achieve real-time and dynamic monitoring of cerebral small vessel blood flow.

[0004] Ultrasonic imaging technology has the advantages of non-invasiveness, real-time, convenience, and strong repeatability, and is an ideal method for cerebral blood flow monitoring. However, existing ultrasonic imaging technology still has some problems in cerebral small vessel blood flow monitoring. First, the imaging resolution is low, making it difficult to clearly show the fine structure and blood flow of cerebral small vessels. Second, the image quality is unstable and easily affected by factors such as patient physiological state and operator skill level, resulting in low signal-to-noise ratio and unclear blood flow signal. In addition, existing ultrasonic imaging technology is not accurate in extracting blood flow dynamic information, and cannot fully reflect the changes in cerebral small vessel blood flow, making it difficult to meet the high-precision requirements of clinical cerebral small vessel blood flow monitoring.

[0005] In recent years, with the continuous development of ultrasonic imaging technology, pulsed Doppler ultrasonic imaging technology has been applied to some extent in cerebral blood flow monitoring. Pulsed Doppler ultrasonic imaging can provide blood flow velocity, direction and other information, but its application in cerebral small vessel blood flow monitoring still faces challenges. On the one hand, the diameter of cerebral small vessels is small and the blood flow velocity is low, making it difficult for traditional pulsed Doppler ultrasonic imaging devices to accurately capture these subtle blood flow signals; on the other hand, the distribution of cerebral small vessels is complex, and imaging from multiple angles and positions is needed to fully understand their blood flow situation, but existing imaging devices and methods have deficiencies in this regard.

[0006] Therefore, developing a method and system for monitoring cerebral small vessel blood flow that can achieve high resolution, high stability, and high precision is of great clinical significance and social value for improving the diagnosis of cerebral small vessel diseases and improving patient prognosis. Summary of the Invention

[0007] In order to solve the problems existing in the prior art, the purpose of this invention is to provide a method and system for monitoring cerebral small vessel blood flow based on ultrasound imaging, so as to meet the high precision requirements of clinical cerebral small vessel blood flow monitoring.

[0008] To achieve the above objectives, the present invention provides the following solution:

[0009] A method for monitoring cerebral small vessel blood flow based on ultrasound imaging, comprising:

[0010] Pulse Doppler ultrasound imaging devices are set up at different locations. The pulse Doppler ultrasound imaging devices are used to perform color pulse Doppler imaging on arteries and veins in small blood vessels of the brain. The devices are arranged according to the spatial positions corresponding to the different locations to obtain three-dimensional color pulse Doppler imaging.

[0011] During the color pulse Doppler imaging process, the current frame color pulse Doppler image is compared and analyzed with the previous frame color pulse Doppler image to obtain the analysis results, which are used to optimize subsequent color pulse Doppler images.

[0012] Hemodynamic information is extracted from the three-dimensional color pulse Doppler imaging, and the changes in blood flow in small cerebral vessels are obtained based on the hemodynamic information.

[0013] Optionally, acquiring the three-dimensional color pulse Doppler image includes:

[0014] A positioning device is installed on the pulse Doppler ultrasound imaging device. The positioning device monitors the three-dimensional target according to the preset monitoring points and divides the three-dimensional target into target blocks of a preset size.

[0015] The pulsed Doppler ultrasound imaging device was used to perform color pulsed Doppler imaging of arteries and veins in small blood vessels of the brain;

[0016] The target blocks corresponding to the small cerebral blood vessels are arranged according to their spatial positions to obtain the three-dimensional color pulse Doppler imaging.

[0017] Optionally, obtaining the analysis results includes:

[0018] The current frame color pulse Doppler image is compared and analyzed with the previous frame color pulse Doppler image to obtain imaging information;

[0019] Calculate the gain-compensated image and the noise-suppressed image using the imaging information:

[0020] ;

[0021] ;

[0022] in, For gain-compensated images, The target value for vascular tissue compensation, The brightness of the blood vessel tissue in the pixel. For noise-suppressed images, To mark pixels, As an inhibitor, These are the pixel coordinates.

[0023] Optionally, optimizing the subsequent color pulse Doppler image includes:

[0024] ;

[0025] in, For the optimized color pulse Doppler image, The image shown is a color pulse Doppler image before optimization.

[0026] Optionally, obtaining the changes in blood flow in the small cerebral vessels includes:

[0027] The hemodynamic information is calculated using a matrix bundle algorithm based on generalized eigenvalues ​​and the principle of matrix rank reduction, thereby obtaining the changes in blood flow in the small cerebral vessels.

[0028] To achieve the above objectives, the present invention also provides a cerebral small vessel blood flow monitoring system based on ultrasound imaging, comprising:

[0029] The pulse imaging module is used to set up pulse Doppler ultrasound imaging devices at different locations, and to use the pulse Doppler ultrasound imaging devices to perform color pulse Doppler imaging on arteries and veins in small blood vessels of the brain, and to arrange them according to the spatial positions corresponding to the different locations to obtain three-dimensional color pulse Doppler imaging.

[0030] The image optimization module is used to compare and analyze the current frame of color pulse Doppler image with the previous frame of color pulse Doppler image during the color pulse Doppler imaging process, obtain the analysis results, and optimize subsequent color pulse Doppler images.

[0031] The information extraction module is used to extract hemodynamic information from the three-dimensional color pulse Doppler imaging, and based on the hemodynamic information, to obtain the changes in blood flow in small cerebral vessels.

[0032] Optionally, the pulse imaging module includes:

[0033] A pulse imaging unit is used to set a positioning device on the pulse Doppler ultrasound imaging device. The positioning device monitors the three-dimensional target according to preset monitoring points and divides the three-dimensional target into target blocks of preset size.

[0034] The pulsed Doppler ultrasound imaging device was used to perform color pulsed Doppler imaging of arteries and veins in small blood vessels of the brain;

[0035] The target blocks corresponding to the small cerebral blood vessels are arranged according to their spatial positions to obtain the three-dimensional color pulse Doppler imaging.

[0036] Optionally, the image optimization module includes:

[0037] The image analysis unit is used to compare and analyze the current frame color pulse Doppler image with the previous frame color pulse Doppler image to obtain imaging information;

[0038] Calculate the gain-compensated image and the noise-suppressed image using the imaging information:

[0039] ;

[0040] ;

[0041] in, For gain-compensated images, The target value for vascular tissue compensation, The brightness of the blood vessel tissue in the pixel. For noise-suppressed images, To mark pixels, As an inhibitor, These are the pixel coordinates.

[0042] Optionally, optimizing the subsequent color pulse Doppler image includes:

[0043] ;

[0044] in, For the optimized color pulse Doppler image, The image shown is a color pulse Doppler image before optimization.

[0045] Optionally, the information extraction module includes:

[0046] The information extraction unit is used to calculate the hemodynamic information based on the matrix bundle algorithm of generalized eigenvalues ​​and matrix rank reduction principle, and to obtain the changes in blood flow in the small cerebral vessels.

[0047] The beneficial effects of this invention are as follows:

[0048] This invention, by setting up pulsed Doppler ultrasound imaging devices at different locations, enables comprehensive, high-resolution color pulsed Doppler imaging of arteries and veins in small cerebral vessels, and acquires three-dimensional color pulsed Doppler images. This provides richer and more accurate image information for comprehensive monitoring of blood flow in small cerebral vessels, making up for the shortcomings of existing ultrasound imaging technologies in terms of resolution and completeness of small cerebral vessel imaging. Furthermore, during the imaging process, the current frame of color pulsed Doppler image is compared and analyzed with the previous frame of color pulsed Doppler image, and subsequent color pulsed Doppler images are optimized accordingly. This effectively improves image quality, enhances image stability and clarity, reduces noise interference, and makes blood flow signals clearer and more distinguishable, further improving the accuracy of small cerebral vessel blood flow monitoring.

[0049] This invention employs a matrix bundle algorithm based on generalized eigenvalues ​​and the principle of matrix rank reduction to extract hemodynamic information from three-dimensional color pulse Doppler imaging. This algorithm can more accurately reflect changes in blood flow in small cerebral vessels, providing clinicians with more reliable hemodynamic parameters, which helps in the early detection of small cerebral vessel lesions and provides a strong basis for early intervention and treatment of the disease.

[0050] The cerebral small vessel blood flow monitoring system of the present invention has a reasonable structure and is easy to operate. It can realize real-time and dynamic monitoring of cerebral small vessel blood flow and has high clinical application value. It can be widely used in the diagnosis, treatment monitoring and related medical research of cerebrovascular diseases, and provides a new technical means for the prevention and treatment of cerebral small vessel diseases. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of a method for monitoring cerebral small vessel blood flow based on ultrasound imaging, according to an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] like Figure 1 As shown, this embodiment discloses a method for monitoring cerebral small vessel blood flow based on ultrasound imaging, including: setting up pulse Doppler ultrasound imaging devices at different locations; using the pulse Doppler ultrasound imaging devices to perform color pulse Doppler imaging on arteries and veins in cerebral small vessels, and arranging them according to the spatial positions corresponding to different locations to obtain three-dimensional color pulse Doppler images; during the color pulse Doppler imaging process, comparing and analyzing the current frame color pulse Doppler image with the previous frame color pulse Doppler image, obtaining analysis results for optimizing subsequent color pulse Doppler images; extracting hemodynamic information from the three-dimensional color pulse Doppler imaging, and obtaining changes in cerebral small vessel blood flow based on the hemodynamic information.

[0056] Specifically, this embodiment discloses a method for monitoring cerebral small vessel blood flow based on ultrasound imaging, including:

[0057] By setting up pulsed Doppler ultrasound imaging devices at different locations, color pulsed Doppler imaging of arteries and veins in small cerebral vessels is performed. These imaging devices are distributed in multiple preset positions, enabling imaging of small cerebral vessels from different angles and depths. The color pulsed Doppler images acquired by these devices are arranged according to their spatial positions to ultimately form a three-dimensional color pulsed Doppler image. This three-dimensional imaging technology can comprehensively and intuitively display the anatomical structure and blood flow distribution of small cerebral vessels, providing basic data for subsequent hemodynamic information extraction.

[0058] In the process of color pulse Doppler imaging, this invention introduces an image contrast analysis and optimization mechanism. Specifically, based on real-time comparison of the current frame of color pulse Doppler image with the previous frame, imaging information is obtained through contrast analysis. This imaging information includes, but is not limited to, changes in blood flow velocity, vessel wall motion, and the signal-to-noise ratio of the image. Based on this imaging information, the system can calculate gain-compensated images and noise-suppressed images, thereby optimizing subsequent color pulse Doppler images. This optimization mechanism can effectively improve image quality, enhance image stability and clarity, reduce noise interference, make blood flow signals clearer and more discernible, and further improve the accuracy of cerebral small vessel blood flow monitoring.

[0059] Extracting hemodynamic information from three-dimensional color pulse Doppler imaging is the core of this invention. The system utilizes advanced algorithms, such as the matrix bundle algorithm based on generalized eigenvalues ​​and the matrix rank reduction principle, to process and analyze the three-dimensional imaging data. Through these algorithms, the system can accurately extract hemodynamic parameters such as blood flow velocity, blood flow rate, and vascular resistance, and based on these parameters, obtain information on changes in blood flow in small cerebral vessels. This hemodynamic information not only reflects the current blood flow state of small cerebral vessels but also allows for monitoring of dynamic changes in blood flow by comparing data from different time points, providing clinicians with more comprehensive and accurate diagnostic information.

[0060] Furthermore, acquiring three-dimensional color pulse Doppler imaging includes: setting a positioning device on a pulse Doppler ultrasound imaging device; the positioning device monitors the three-dimensional target according to preset monitoring points while dividing the three-dimensional target into target blocks of preset size; using the pulse Doppler ultrasound imaging device to perform color pulse Doppler imaging on arteries and veins in small cerebral vessels; arranging the target blocks corresponding to the small cerebral vessels according to their spatial positions to acquire three-dimensional color pulse Doppler imaging.

[0061] Specifically, the process of acquiring three-dimensional color pulse Doppler imaging in this invention is a highly precise and systematic procedure designed to ensure the comprehensiveness and accuracy of the imaging. The detailed steps are as follows:

[0062] Setting up a positioning device on a pulsed Doppler ultrasound imaging system is a crucial step in achieving three-dimensional imaging. The main function of the positioning device is to ensure that the imaging device can accurately align with preset monitoring points and monitor the three-dimensional target. These monitoring points are pre-set based on the anatomical structure and blood flow distribution of the small blood vessels in the brain, covering key areas of the small blood vessels, including arteries and veins.

[0063] The positioning device can not only accurately align with monitoring points, but also divide the three-dimensional target into target blocks of preset sizes. The size of these target blocks is pre-set according to imaging requirements and the anatomical features of small cerebral vessels to ensure that each target block contains sufficient information while avoiding information overload. For example, the size of the target blocks can be adjusted according to the diameter and blood flow velocity of small cerebral vessels to ensure imaging resolution and accuracy.

[0064] Color pulsed Doppler ultrasound imaging was used to perform color pulsed Doppler imaging of arteries and veins in small cerebral vessels. Color pulsed Doppler imaging is an advanced ultrasound imaging technique that can display the direction and velocity of blood flow in real time. Using this technique, the system can acquire blood flow information from arteries and veins in small cerebral vessels and present it as color images. These color images not only visually display the direction of blood flow but also use different colors to represent the velocity of blood flow, thus providing rich information for subsequent analysis.

[0065] During the imaging process, the system automatically adjusts imaging parameters to ensure high-quality images are acquired. These parameters include the frequency of the ultrasound waves, pulse repetition rate, and gain. By automatically adjusting these parameters, the system can adapt to different imaging conditions, ensuring image clarity and stability.

[0066] The target blocks corresponding to small cerebral blood vessels are arranged according to their spatial location to form a three-dimensional color pulse Doppler image. This process is achieved by arranging and combining multiple two-dimensional images according to their positions in three-dimensional space. The image of each target block is acquired from different angles and depths, and through precise spatial positioning and image processing techniques, these images are combined into a complete three-dimensional image.

[0067] When constructing 3D images, the system uses advanced image processing algorithms, such as interpolation and fusion algorithms, to ensure image smoothness and continuity. These algorithms effectively handle overlapping areas between images, eliminate artifacts and noise, thereby generating high-quality 3D color pulse Doppler imaging.

[0068] More specifically, the accuracy of the positioning device is crucial to ensuring the quality of 3D imaging. Positioning devices typically employ high-precision mechanical and electronic systems capable of precisely controlling the position and angle of the imaging device. For example, positioning devices can utilize robotic arms or robot systems, which can accurately position and move based on preset monitoring points. Furthermore, positioning devices can be equipped with sensors, such as optical or electromagnetic sensors, to monitor the position and orientation of the imaging device in real time, ensuring imaging accuracy.

[0069] In color pulsed Doppler imaging, optimizing imaging parameters is crucial for ensuring image quality. The system automatically adjusts these parameters, such as ultrasound frequency, pulse repetition rate, and gain, based on different imaging conditions. For example, in areas with high blood flow velocity, the system increases the ultrasound frequency to improve imaging resolution; in areas with low blood flow velocity, the system decreases the ultrasound frequency to reduce noise interference. Through this automatic adjustment mechanism, the system can adapt to different imaging conditions, ensuring image clarity and stability.

[0070] When constructing a 3D color pulse Doppler image, the system employs advanced image processing algorithms. These algorithms, including interpolation and fusion algorithms, effectively handle overlapping areas between images, eliminating artifacts and noise. For example, interpolation algorithms can be used to fill in blank areas between images, generating smooth 3D images; fusion algorithms can be used to merge multiple images to produce high-quality 3D images. The use of these algorithms not only improves image quality but also reduces imaging time and increases system efficiency.

[0071] Furthermore, obtaining the analysis results includes: comparing and analyzing the current frame's color pulse Doppler image with the previous frame's color pulse Doppler image to obtain imaging information; and using the imaging information to calculate a gain-compensated image and a noise-suppressed image.

[0072] ;

[0073] ;

[0074] in, For gain-compensated images, The target value for vascular tissue compensation, The brightness of the blood vessel tissue in the pixel. For noise-suppressed images, To mark pixels, As an inhibitor, These are the pixel coordinates.

[0075] Further, optimizing the subsequent color pulse Doppler image includes:

[0076] ;

[0077] in, For the optimized color pulse Doppler image, The image shown is a color pulse Doppler image before optimization.

[0078] Specifically, obtain the analysis results and optimized images:

[0079] In this invention, obtaining the analysis results and optimizing subsequent color pulse Doppler images are crucial steps in ensuring image quality and stability. The detailed steps are as follows:

[0080] During color pulse Doppler imaging, the system compares the current frame of the color pulse Doppler image with the previous frame in real time to obtain imaging information. This process includes the following key steps:

[0081] Image acquisition: The system continuously acquires multiple frames of color pulse Doppler images, each frame containing blood flow information of small blood vessels in the brain.

[0082] Comparative analysis: The current frame image is compared pixel by pixel with the previous frame image to analyze changes in blood flow velocity, blood flow direction, and vessel wall motion. Through this comparative analysis, the system can acquire imaging information, including but not limited to: changes in blood flow velocity, vessel wall motion, image signal-to-noise ratio, and pixel brightness changes in vessel tissue. This imaging information provides the basic data for subsequent image optimization.

[0083] Using imaging information, the system calculates gain-compensated and noise-suppressed images to improve image quality and stability. The specific calculation formula is as follows:

[0084] Gain-compensated image:

[0085] ;

[0086] in, For gain-compensated images, The target value for vascular tissue compensation, The brightness of the blood vessel tissue in the pixel. These are the pixel coordinates.

[0087] Noise-suppressed image:

[0088] ;

[0089] in, For noise-suppressed images, It is an inhibitory factor.

[0090] The specific meanings of these calculation steps are as follows:

[0091] Gain-compensated image: calculated by comparing the target value T with the current pixel brightness. The difference is used to generate a gain-compensated image. This compensation image can adjust the brightness of the image, making the vascular tissue appear clearer.

[0092] Noise-suppressed image: by marking pixels and inhibitory factors Generate noise-suppressed images This image effectively suppresses noise and improves the signal-to-noise ratio.

[0093] The system optimizes subsequent color pulse Doppler images using gain-compensated and noise-suppressed images. The specific optimization formula is as follows:

[0094] ;

[0095] in, For the optimized color pulse Doppler image, The image shown is a color pulse Doppler image before optimization.

[0096] The specific steps of the optimization process are as follows:

[0097] Image fusion: The unoptimized color pulse Doppler image B(i,j) is fused with the gain-compensated image F(i,j) to generate a new image.

[0098] Noise suppression: The fused image is combined with the noise-suppressed image N(i,j) to further suppress noise.

[0099] Final optimization: Through the above steps, an optimized color pulse Doppler image A(i,j) is generated.

[0100] The accuracy of image contrast analysis is crucial to ensuring the accuracy of imaging information. The system employs high-precision image processing algorithms to compare the current frame with the previous frame pixel-by-pixel, ensuring the accuracy of the contrast analysis. These algorithms are capable of processing large-scale data and extracting imaging information quickly and accurately.

[0101] Gain compensation and noise suppression are important methods for improving image quality. By calculating gain-compensated and noise-suppressed images, the system can adjust the brightness and signal-to-noise ratio of the image, making the display of vascular tissue clearer. These calculation steps not only improve image quality but also reduce noise interference and enhance image stability.

[0102] The efficiency of the image optimization process is crucial for ensuring the real-time performance of the system. By fusing gain-compensated and noise-suppressed images, the system can quickly generate optimized color pulse Doppler images. These optimization steps not only improve image quality but also reduce imaging time and increase system efficiency.

[0103] Furthermore, obtaining information on changes in cerebral small vessel blood flow includes: calculating hemodynamic information using a matrix bundle algorithm based on generalized eigenvalues ​​and the principle of matrix rank reduction, thereby obtaining information on changes in cerebral small vessel blood flow.

[0104] Extracting hemodynamic information from three-dimensional color pulse Doppler imaging is the core of this invention. The system utilizes a matrix beam algorithm based on generalized eigenvalues ​​and the matrix rank reduction principle to process and analyze the three-dimensional imaging data. Specifically, the system first converts the three-dimensional imaging data into matrix form, and then extracts hemodynamic information using the matrix beam algorithm. This algorithm can effectively process large-scale data and extract accurate hemodynamic parameters, such as blood flow velocity, blood flow rate, and vascular resistance. Through these parameters, the system can comprehensively reflect the blood flow status of small cerebral vessels and monitor their dynamic changes.

[0105] Specifically, the hemodynamic information is extracted from the output of the matrix beam algorithm, and the specific steps are as follows:

[0106] Eigenvalue analysis: This involves analyzing the magnitude and distribution of generalized eigenvalues ​​to extract key eigenvalues ​​that reflect changes in blood flow. These eigenvalues ​​provide important parameters such as blood flow velocity and blood flow rate.

[0107] Eigenvector analysis: This involves analyzing the direction and amplitude of generalized eigenvectors to extract key eigenvectors that reflect the direction and intensity of blood flow. These eigenvectors provide information about the direction and intensity of blood flow.

[0108] Parameter calculation: Based on the extracted feature values ​​and feature vectors, specific hemodynamic parameters, such as blood flow velocity, blood flow rate, and vascular resistance, are calculated.

[0109] This embodiment also provides a brain small vessel blood flow monitoring system based on ultrasound imaging, including: a pulse imaging module, used to set pulse Doppler ultrasound imaging devices at different locations, and use the pulse Doppler ultrasound imaging devices to perform color pulse Doppler imaging of arteries and veins in brain small vessels, and arrange them according to the spatial positions corresponding to different locations to obtain three-dimensional color pulse Doppler images; an image optimization module, used to compare and analyze the current frame color pulse Doppler image with the previous frame color pulse Doppler image during the color pulse Doppler imaging process, obtain the analysis results, and use them to optimize subsequent color pulse Doppler images; and an information extraction module, used to extract hemodynamic information from the three-dimensional color pulse Doppler imaging, and obtain the changes in brain small vessel blood flow based on the hemodynamic information.

[0110] Furthermore, the pulse Doppler ultrasound imaging device includes: an ultrasound transmitting device, an echo receiving and processing device, an FPGA main control device, and an ultrasound imaging transducer. The ultrasound transmitting device is configured to generate a high-voltage pulse signal to excite the ultrasound imaging transducer to generate an ultrasonic pulse signal, and includes a high-voltage pulse circuit and a transceiver isolation circuit. The echo receiving and processing device is configured to pre-amplify, gain-compensate, and perform analog-to-digital conversion on the echo signal, transmitting the converted digital signal to the FPGA main control device. The FPGA main control device is configured to receive and store echo data, perform dynamic digital filtering, envelope detection, logarithmic amplification, secondary data sampling, and communication with a host computer display device. The ultrasound imaging transducer is configured to convert the high-voltage pulse signal into an ultrasonic signal, and then convert the ultrasonic echo signal into an electrical signal for transmission to the echo receiving and processing device.

[0111] The workflow of a pulsed Doppler ultrasound imaging device includes the following steps: A high-voltage pulse circuit in the ultrasound transmitter emits a precisely delayed high-voltage pulse signal based on ultrasound focusing; the high-voltage pulse signal is transmitted to the ultrasound imaging transducer via a transceiver isolation circuit; the ultrasound imaging transducer converts the high-voltage pulse signal into an ultrasound signal to scan the three-dimensional target; the ultrasound imaging transducer converts the reflected echo signal from the three-dimensional target into an electrical signal and transmits it to the echo receiving and processing device; the echo receiving and processing device performs low-noise amplification, variable gain amplification, anti-aliasing filtering, and high-speed analog-to-digital conversion on the ultrasound echo signal, and transmits the digital signal after analog-to-digital conversion to the FPGA main control device; the FPGA main control device receives and stores the digital signal, processes it, and then transmits it to the information extraction module.

[0112] Furthermore, the pulse imaging module includes: a pulse imaging unit, used to set a positioning device on the pulse Doppler ultrasound imaging device, wherein the positioning device monitors the three-dimensional target according to preset monitoring points and divides the three-dimensional target into target blocks of preset size; color pulse Doppler imaging of arteries and veins in small cerebral vessels is performed using the pulse Doppler ultrasound imaging device; and the target blocks corresponding to the small cerebral vessels are arranged according to spatial position to obtain three-dimensional color pulse Doppler imaging.

[0113] Furthermore, the image optimization module includes: an image analysis unit, used to compare and analyze the current frame's color pulse Doppler image with the previous frame's color pulse Doppler image to obtain imaging information; and to calculate a gain-compensated image and a noise-suppressed image using the imaging information.

[0114] ;

[0115] ;

[0116] in, For gain-compensated images, The target value for vascular tissue compensation, The brightness of the blood vessel tissue in the pixel. For noise-suppressed images, To mark pixels, As an inhibitor, These are the pixel coordinates.

[0117] Further, optimizing the subsequent color pulse Doppler image includes:

[0118] ;

[0119] in, For the optimized color pulse Doppler image, The image shown is a color pulse Doppler image before optimization.

[0120] Furthermore, the information extraction module includes an information extraction unit, which is used to calculate hemodynamic information based on the matrix bundle algorithm of generalized eigenvalues ​​and matrix rank reduction principle, and to obtain the changes in blood flow in small cerebral vessels.

[0121] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for monitoring cerebral small vessel blood flow based on ultrasound imaging, characterized in that, include: Pulse Doppler ultrasound imaging devices are set up at different locations. The pulse Doppler ultrasound imaging devices are used to perform color pulse Doppler imaging on arteries and veins in small blood vessels of the brain. The devices are arranged according to the spatial positions corresponding to the different locations to obtain three-dimensional color pulse Doppler imaging. During the color pulse Doppler imaging process, the current frame color pulse Doppler image is compared and analyzed with the previous frame color pulse Doppler image to obtain the analysis results, which are used to optimize subsequent color pulse Doppler images. Hemodynamic information is extracted from the three-dimensional color pulse Doppler imaging, and the changes in blood flow in small cerebral vessels are obtained based on the hemodynamic information.

2. The method for monitoring cerebral small vessel blood flow based on ultrasound imaging according to claim 1, characterized in that, Acquiring the three-dimensional color pulse Doppler image includes: A positioning device is set on the pulse Doppler ultrasound imaging device. The positioning device monitors the three-dimensional target according to the preset monitoring points and divides the three-dimensional target into target blocks of preset size. The pulsed Doppler ultrasound imaging device was used to perform color pulsed Doppler imaging of arteries and veins in small blood vessels of the brain; The target blocks corresponding to the small cerebral blood vessels are arranged according to their spatial positions to obtain the three-dimensional color pulse Doppler imaging.

3. The method for monitoring cerebral small vessel blood flow based on ultrasound imaging according to claim 1, characterized in that, Obtaining the analysis results includes: The current frame color pulse Doppler image is compared and analyzed with the previous frame color pulse Doppler image to obtain imaging information; Calculate the gain-compensated image and the noise-suppressed image using the imaging information: ； ； in, For gain-compensated images, The target value for vascular tissue compensation, The brightness of the blood vessel tissue in the pixel. For noise-suppressed images, To mark pixels, As an inhibitor, These are the pixel coordinates.

4. The method for monitoring cerebral small vessel blood flow based on ultrasound imaging according to claim 3, characterized in that, Optimizing the subsequent color pulse Doppler image includes: ； in, For the optimized color pulse Doppler image, The image shown is a color pulse Doppler image before optimization.

5. The method for monitoring cerebral small vessel blood flow based on ultrasound imaging according to claim 1, characterized in that, Obtaining the changes in blood flow in the small cerebral vessels includes: The hemodynamic information is calculated using a matrix bundle algorithm based on generalized eigenvalues ​​and the principle of matrix rank reduction, thereby obtaining the changes in blood flow in the small cerebral vessels.

6. A cerebral small vessel blood flow monitoring system based on ultrasound imaging, implemented according to any one of claims 1-5, is characterized in that, include: The pulse imaging module is used to set up pulse Doppler ultrasound imaging devices at different locations, and to use the pulse Doppler ultrasound imaging devices to perform color pulse Doppler imaging on arteries and veins in small blood vessels of the brain, and to arrange them according to the spatial positions corresponding to the different locations to obtain three-dimensional color pulse Doppler imaging. The image optimization module is used to compare and analyze the current frame of color pulse Doppler image with the previous frame of color pulse Doppler image during the color pulse Doppler imaging process, obtain the analysis results, and optimize subsequent color pulse Doppler images. The information extraction module is used to extract hemodynamic information from the three-dimensional color pulse Doppler imaging, and based on the hemodynamic information, to obtain the changes in blood flow in small cerebral vessels.

7. The method for monitoring cerebral small vessel blood flow based on ultrasound imaging according to claim 6, characterized in that, The pulse imaging module includes: A pulse imaging unit is used to set a positioning device on the pulse Doppler ultrasound imaging device. The positioning device monitors the three-dimensional target according to preset monitoring points and divides the three-dimensional target into target blocks of preset size. The pulsed Doppler ultrasound imaging device was used to perform color pulsed Doppler imaging of arteries and veins in small blood vessels of the brain; The target blocks corresponding to the small cerebral blood vessels are arranged according to their spatial positions to obtain the three-dimensional color pulse Doppler imaging.

8. The method for monitoring cerebral small vessel blood flow based on ultrasound imaging according to claim 6, characterized in that, The image optimization module includes: The image analysis unit is used to compare and analyze the current frame color pulse Doppler image with the previous frame color pulse Doppler image to obtain imaging information; Calculate the gain-compensated image and the noise-suppressed image using the imaging information: ； ； in, For gain-compensated images, The target value for vascular tissue compensation, The brightness of the blood vessel tissue in the pixel. For noise-suppressed images, To mark pixels, As an inhibitor, These are the pixel coordinates.

9. The method for monitoring cerebral small vessel blood flow based on ultrasound imaging according to claim 8, characterized in that, Optimizing the subsequent color pulse Doppler image includes: ； in, For the optimized color pulse Doppler image, The image shown is a color pulse Doppler image before optimization.

10. The method for monitoring cerebral small vessel blood flow based on ultrasound imaging according to claim 6, characterized in that, The information extraction module includes: The information extraction unit is used to calculate the hemodynamic information based on the matrix bundle algorithm of generalized eigenvalues ​​and matrix rank reduction principle, and to obtain the changes in blood flow in the small cerebral vessels.

Images